{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import * from utils\n",
    "\n",
    "rat_name = 'R'\n",
    "mod_name = '1'\n",
    "sess_name = 'OF'\n",
    "day_name = 'day1'\n",
    "\n",
    "dim = 6\n",
    "ph_classes = [0,1] # Decode the ith most persistent cohomology class\n",
    "num_circ = len(ph_classes)\n",
    "dec_tresh = 0.99\n",
    "metric = 'cosine'\n",
    "maxdim = 1\n",
    "coeff = 47\n",
    "active_times = 15000\n",
    "k = 1000\n",
    "num_times = 5\n",
    "n_points = 1200\n",
    "nbs = 800\n",
    "\n",
    "sspikes = get_spikes(rat_name, mod_name, day_name, sess_name, bType = 'pure',\n",
    "                                 bSmooth = True, bSpeed = True)[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_neurons = len(sspikes[0,:])            \n",
    "times_cube = np.arange(0,len(sspikes[:,0]),num_times)\n",
    "movetimes = np.sort(np.argsort(np.sum(sspikes[times_cube,:],1))[-active_times:])\n",
    "movetimes = times_cube[movetimes]\n",
    "\n",
    "ssspikes = preprocessing.scale(sspikes[movetimes,:],axis = 0)\n",
    "\n",
    "dgms = {}\n",
    "for i in np.arange(3,41):\n",
    "    dim_red_spikes_move_scaled,__,__  = pca(ssspikes, dim = i)\n",
    "    indstemp,dd,fs  = sample_denoising(dim_red_spikes_move_scaled,  k, \n",
    "                                        n_points, 1, metric)\n",
    "\n",
    "    X = squareform(pdist(dim_red_spikes_move_scaled[indstemp,:], metric))\n",
    "    knn_indices = np.argsort(X)[:, :nbs]\n",
    "    knn_dists = X[np.arange(X.shape[0])[:, None], knn_indices].copy()\n",
    "    sigmas, rhos = smooth_knn_dist(knn_dists, nbs, local_connectivity=0)\n",
    "    rows, cols, vals = compute_membership_strengths(knn_indices, knn_dists, sigmas, rhos)\n",
    "    result = coo_matrix((vals, (rows, cols)), shape=(X.shape[0], X.shape[0]))\n",
    "    result.eliminate_zeros()\n",
    "    transpose = result.transpose()\n",
    "    prod_matrix = result.multiply(transpose)\n",
    "    result = (result + transpose - prod_matrix)\n",
    "    result.eliminate_zeros()\n",
    "    d = result.toarray()\n",
    "    d = -np.log(d)\n",
    "    np.fill_diagonal(d,0)\n",
    "    thresh = np.max(d[~np.isinf(d)])\n",
    "    rips_real = ripser(d, maxdim=maxdim, coeff=coeff, thresh = thresh, \n",
    "                       do_cocycles=False, distance_matrix = True)\n",
    "    dgms[i] = rips_real[\"dgms\"]\n",
    "    print(i)\n",
    "    \n",
    "np.savez('pca_analysis_R1', dgms_all = dgms)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, ''), Text(0, 0, '')]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kjSUQqsPrUmkOaYNUSiFEX2S0+RPRTYKqjtJh3U9sJ/2kzaDcIjhxmMK8IupOhBkW8A5WUYUQKcpoTSWsGQx16b0vJozJK4LmwzICJISDZTRUIrpJsC+hklsEJw5RmCsbYAvhVBmvqfR2OHsneWPtmkqeT7ZAEMKhMhsqukFATRzO3qv2PhWZqyKEc2W4pmLaoZJqTSXX7lOR5o8QzpX55o/Sy5k/HQWGgxHljBxdOmqFcKiMDil/uO8zzgyHqGyIElt3vHTp0u4foCiQewZjlM+kpiKEQ2WsprKmsp7fvLsPQwvx9MY61lTWp/bAvCKGaQ2cCGtEdGNgCymE6LOMhUp5VQNRw8RPlJO6m/KqhtQemDsWV8unjBzi4+gJGQESwmkyFiqlkwpQFfChYbr8lE4qAGDx4sUsXry4+wfmSWetEE42IH0qayrrKa9qoHRSAfOnFSa9pnTSSNwulZI8F9+eNZXZ7dfV1tb2/OS5RfYOcDKsLIQjpb2msvrjOsqeqeDpD2u4f/mWbvtKdhxuZsrooUzKdzN74ujUXyA+AU6m6gvhRGkPlXXVjZjtuxKENKPbvpKNB5qYUzIc9HDqQ8qQWFQozR8hHCntoVI6qQCPqgCQ43HF+0pOVXHgGBeMzwc90rdQiS8q9FKXpKN2TWU9P35lZ+qjSUKItEp7qMyfVsjDN5yD363yq9tmJu1TMU2LTTXHmD0+H/RQ6jNqAfx5oKic4Y9Sf0rzZ01lPfc9t7nXppcQYuAMyOjP7RcWM3Z4gNG5yWsgVUdbyA94GDXUb9dUUl37E5NXRJHyWZeO2vf3HiWs24eO9dT0EkIMnAGbUXvF2QW8u6eB88YN63LfxgNNzBk/3P7LKX0q3e5R21FuESOtRupPKFiWhaLYzS3DhKG0Uag0EVANSifNSct7EUKkbsDmqcybMop39x5Nel/FgSa7PwXglMPEFi5c2P2O+jF5Rfhbj+Bzqxxvs7eV3HbwOG9ur+Vl/0/5v+5nyOMk81U5HlWIwTZgoTJnfD7V9S00tUa73LfxwDG7pmJZfR/9gcQWCHn2XJXjbVH+9rnN/OzMnUzkIJerO6g2CmnY1c1et0KIATNgoeJzu5g7YUSXfo1Pj4cIaQYTRgbB0OwzklVX/P6amhpqamp6fvIOWyDUNYf5+xe2MX9aIdfOnQGqC0WBv/O8xEq+NBBvTQjRgwGdpn/FZLtfpaOKmmPMKcm3+0H0UJdaypIlS1iyZEnPT5xn11RM0+LvV2zjk8ZWfnTtVJh8LeQMh3EXce3Q/bx6pGt/jhBiYA14qLy/twHTTJzRY/enxDppI+DpY9MHIHcsbY01fPRJE02tUQ4fD/He3gZorAKXF+5aRdDvYWjjFg42taXp3QghUjGgoTI2P8DwoJcdh5vjt9n9Ke2dtP3pTwHIK8LTWodu2sPHEd20m1lVq+2D3FUVZfY3uS9vHa9t/zQdb0UIkaIBX6V8xeQC3tljjwKdCGvUfNbK9DPy7DtPGflJmTcInhzO8Ni1kPjM3eo1MOkv7Gtm3sl5LetYu3lvOt6GECJFAx4q8yaPiverbK45xrlj8/C6219WD4O7jxPf2nnyx/HINSP5xsUlPHr7+cyfGIRDFXDm5fYFwZG4Jl/NJa1vsbf+ZDreihAiBQMeKnPGD2dfgz20bK/3GZ64U4/0r6YCkFvERSNCPHz9OfZSgE/eg7FzwDckfoky5y6+7nmbV7ccPs13IYRI1YCHitetclH70HKnmbSQdPQnZe0LC+OqVieaPjEllzLU7+bAlrfkQHchBsmg7Px2xeQCVn9cz47Dzcwq7jDM+8k6OH4Adq+M37Ro0SIWLVrU+5O2n1YI2JPoqtbAWfM7X6Mo+Ob+Ndcbq9l2qLnLU8iKZiHSb5BCZRQrd9rT6tfvb7Jv3L0SPngEmg/Bi38dD5aSkhJKSkp6f9L2zZoAOFppT6IbOanLZcp5t1NqbeLJNRWdAmT1x3WyolmIATAoR3RUfnoCLDjWpnH/8i12x+ona+0ZtQBaCPathSkLUn/S9qn6QKLp076wsJPAcA4XzmPMvhVUVRfxk/XjWTx8HIeOhdDb58/EVjR3t/Vltktle08hn1O6DEpNpbyqgViPRnxLgnFzExd4cmDilQAsW7aMZcuW9f6kHftUqtZ07U/poJqx3O1+g4nKp/yN62WuHrqfX9x6Hv72USi3qnS7mVS2kz1mUrOmsp7vLNvE0x/WcN9zm+VzOg2DEiqlkwrI8djre+JzSowIjL0ALrgbbn4iXkspLy+nvDyFhYBDz4CTR6CtCY5sh/Hdr/OZ6m9mhHKSRZ7lfN39NncGN3HD+UU8dscs/mrOWPKDXo63dV342Bun98lYlsWzH9V02mPm/W5Wjn/RPf7+vnjNNayb8jmdhkFp/syfVsijt5/fuWr5zItw0XfhnJv696Qev70L3PYXoPgi8Aa6vbT4wuswDryIy4xgqB6KL/hKvFzzpxVSfbSFry39kJIRQS48c3i3z9NR7DebblqsqDhkN+kcVGWu/ayNn7z2MbuPnMTrUokaJgr22qtPGls5c2Qw00VM2UA3S17Zepjqoy343CoR3f6cWiNyUF1/DUyo7F5p95FMvDJeA4l9gQFoaYBDm+Brz57e6+QVwaYnYc63er5uygJcf/UUbH4GV+2fYcy5ne4+a9QQfvm1mXz32c289J1LKB7RfUDFPPtRjSP7ZFbuOMLS9/axr6GV786byH8tnM17exsor2rgkokjOXSsjZt+/QGXnV3AEJ+bKyaPckS5IREeF00YwcUTRtAS0XlrVz3/b9VuIro5IOH97p6j/PPrlTx/z8XUNrVRXtXAjKI8fvVWFSsqDnLrnHFpe60vivSHyq7XYcU3wDRg67JOTZu4ypfh7L/osXaREtUDDdvs//dmygL7v3W/ghe+AXet6jTx7rKzC7jvyrP42uMfcvnZBVw1tbDbH96WiM72g83xGoDXpTqiT2bVjiP87XObsSzwuVUmjRqK1612DnRgiM/Noj/twLTg+Y0HueuS8Xy7dAKj8/ynVSvoy2M7Xjtz3DAefbuKZz+qwbTg6Q9rCHhd5Ae8tEZ0IqdsEZqOcq2prOelzYcor2rk9399AZNHD2Xy6KHx+88vzue2x9dT29RGc0iTzts+SH+o7H/XDhToflRn54tw6QOn9zq7V8KRLfafV/8IhhamNnp06ffg8CZY9Q9w3b93umtMnp+jJyM8v/EgL285zGN3zEr6g/TI6r1cOXUUV08fze/W7edkWHfED9xja6uJzfGLLbJMVq7KIyfix6gYpsXqyjpe3HwIt0ulqSWKYVm8sPFg/P1396Usr2pgVnE+hbl+Xt56mD9WHMKwLP6w4SD/cWfyzy722Pue20xYN1m2vgaf20Vhro8Oi9m5ZfZYHr7+nE7XAvG+uVS9ubOOe5/bjGZaLN9QS9nlEwl63fxyzd74L4SmVq3L484aNYS7S8/k56t2AziyietU6Q+ViVfClmfa1/X44qM6cccPQsPurrf31b61vYdXMooCN/wafnsVvPH9RJmnLGBddSNGh8669/Yc7fJDtONQM69uO8zqBy9neNDL5WcXMO8X79qnA5TkJ33JwRiqfG9vA0eaQ/jdKmHd7PF4lNJJBayoOERIM8jxuPinr0znqimj+N7zW3ht+xHAfv//8MdtXHjmcN7ZbZ97vXxDLV859wwaWyJ80H6+09Mf1jCxIIhbVTDaEy1imPzm3WqumjIKVe08zG+aFk+s2x8PCdOCm2YVccXkUdy/fEu8TLGyz59WyGN3zKK8qoGx+Tks33CQhpYIl59dwKaaY71+pv/1/j609n9TzbD40+bDRHSDqGG/ftToPnwPHw/F/xzSDP5n5xEJlRSkf/RnygK45UkouRTyJ9gbJ3X08Z9g6nXg9iZ9eHFxMcXFxb2/zsQrE7vwdxiSTolvKFx4D2z8rf1f++S7jqNUqgJ7j7bEQwZAN0x+9Kft/PDaqQwP2uX3ulX+dt5Z/Oqt5KuhB2NIt+FkhB+s2MZ/3jmLx+6YlVhk2c0XINZx3vE6VVX46syi+Pv3u1XuLp3AkeZw/AuoGRZ7608S1c1OtYpLzxrJ96+eEn+sz61yMqzz9Sc+6nTgW/XRk9z2+HrqTkTwtQ/n53hc8X6dU8vUsbwPX38O91w2kTfu/xKNLVEeeH5rr5/pltpj8Q7Y2Gv95KvT+flN53YdjUyi48+DW1VYtbOOH720nQONrY4f+cskpS9rYubMmWNVVFSkdrFpwOOXw5cehHNuTty+9DKY/zBMuKJPBU0qSYdwyt74vh0oMRfcDX/5i3itYu6ZI3hm/QHGjwjy85tmoCgKT6z7hNWVdSy/+6L4Dv4AUd1k3i/e5dHbZzK7pPPo0Q9f3M7zGw/G//6Ni0t4+Ppz+vV2kzFNi7ue2sg5Rbn84Oopp/18p9aq1lTWd6pBPHr7+QBdbju1mTRvcgH/8U41y9bXcuvssXxQ3cj+xlb+4ZrJ3Dm3hLW7j/a79vbjV3by9IeJLUeTfaYnwxp/+eg6Fi2YgktVu22+9fb6Ha+bXZLPUx98whMfHCAUNTAsq9P7/yJRFGWTZVlJj6sYuFABqPkQXvw23LvB3gOlsRqeWgB/t6vTvrQZsXulXUPRQoACNy6F877W6ZKWiM7Xf/cRwwNehgU8vPlxPa/ceykTC4Z0ebrlG2pZueMIz3wrMamvOaRx/X+s4+CxULzGs/iGc7jzohSWIaTov8v388aOI7xQdjEeVzcVz9MJX7rv6EzlS/nrd6r51zf3YGHXfrrrp+preWKhBvDQgqncfdmE+P2WZfHAH7YS9LlZcuOM03qtZPryi+LzOku3p1AZ2HkqJRdDySVQ/m9w1Y/tDtppN2Q+UMD+ct38hP1lO1kHFU/AtK92OthsiM/NNy8ZzwPPb8XCrgLvb2hNhEqHL+vNs67hP9+ppqJ9Jfbxtihf/90G5k0ZxVd924jsfYu1gQU8+1EtN88ei7+PHY6nWlNZz8tbDvHe3kZWfa+0+0DZ9Tr88S4wot2PxvXi1NGj7m5Lpu5EOD6bOtxD53FfyxOb9zTU72Hp+/u4aMIIZoy1N/96afNhKj89wav3tk+ITBaqpxG0V00t5JWtn8ZDLaqbnc6fiukYfl+kjt6Bn/w2/2H4zSUw807Y+Ue4/j97vLysrAyApUuXDnjR4sPMpgkvfRv+VAa3PAVq4gu6qeZY/Euhm1biS7F7Jfzxm/aeMFuX4b35Ce6ddw4/fuVjzinKZf3+Jq6eXsiiCZ+gvPgg6GHmuldy78jf8dPXPubnN52brEQp/WZ7bdun/N0LW9EMC49LYXfdScYNbx+e370Sqt+2F1y21MHmp+1Agf6tsTpNp3YKp2vovWOonTs2j7ue2sDfXD6RnYebeWvXUVb8zcXkeF2w/r/gf34IWLDxvyG/xJ6C0LQPLBM2/x5u/C2cc0PKQdMx1KaMzuWZ9TU8+IetfHlqIRsONPGls0YyOs/Po2/vjQePk+YyDbSBD5XcMfClB+Dp6yF0zJ745jSqCjf8Bp65Ef5wp71Ysf0Hq9svxaYn7UAB+8ta/TZ5489n15ETVB45gVtVuGD8cJTNP7VHwgBFD/EvI1Zy3f5beWnzIW6aNTZeBN0w+Zc39/Db8v1YFjy7vpbbLhzHbRcUc+hYG+uqG8nN8VD7WRurK+vIM44xT93CMSuX8qpi5pe44M+PwYePtY+KKXZf1pcfhrf+KdHMG3fhoH6086cVsvyyJsJ73sI/+cvMjH2p0lh7uHr6aLYePM7P3tgFgEdVOHQsxFT1ILz9E4j/WrBgzEz78/msyr7JiNq/UNb9m73a3dRTqtF1DLUbzy/irqc2ct/yLVjAMx/WMCrXx/nj8tlb10LE6Hk07vNmUKbpM+xMe4sDLHjpW/2qgg84tw9mfxNeugew7GHxW55k/rQFXZcY7HodateDy2evYVIU+OQ9Dpy8mBnKYQqUZkZxjJI3HwNrv73DvxEF1U2w6mWWn1/Mda+34a5ZR97Bt/lgyNWsahxOWDOZQTUXuXbxvnku6/cHeHdPQ3xoU1Xgzrkl/O7SZi746HvkKBq6paLtWQ6VLfayhdgwOxbk5MPcu+2Zx/vW2i2XSMUAAAWJSURBVP8GW56B6TeCK4UJg+mweyUzP7Jrahx/A4Zrdg3hf/7RDuXNv4cLvg3hE7D9eftLvekpmLcILviW/Z5SCJvWiB7/s2Za7Nn+EfMPfd/ugN/4WztUPTlwbnu/2b63Erdd9xhsex7M7e1PEILKV3r+Ge1QppwpC5g0Ksj6/Z8BdoRdPX10fJ7N57FPpSeDEyoH3if+2yIDVfCUHdxAvJx62O4DmrKgc//Bxy/Dyh/A/3kNTnxqv5cJV0DoGN9e9SB3e1twKyaWBZ/m3whffwWq30p8KQomU7jqH1nleoKfbbyBFquIUtdy/nuUyRjvEYZEd6Eq8I/W8xzN/wrvuC5lc/M+pqo1RPEy65CXua3vgGJP2HIrJu6SC+GW39lbQMQ6nzsOs8ebeQY8fwe8/iB89bHkW0Uk09/+h4a9sPaf4zU19DC8+SNASdTyjCjsfdMur9keDKYG638N7//Cvj3UZAdRe9AnK0PHGuW5nsPcfeBf4S9/DjNugeKLu5Y/1p8Wu80bhNoP7M9OdcPu1+G1B+CMWVC3PXGdZcH2FfDavfGmLzc/wWVnz+aPmw4nnWfzRQmTmIEd/YnpONLiyemxpjKofSqn6lhO1WPXXi65Hwqm2sHo9sOOF2DhizA6yajCK/fBlqcTf28fpu7CstjzyLUUntiGD40cRaMmMIOSojOg6s3EdaPPozliMLRpJ6oChqVQd+aNFJ09C9b+zP6Snvp59hYAkRZ48hoonGF/kU69ruPjJ1wBO1bAqh+07yfsT3ypu2u+VL8NgXx71nLdDhhfCntWdi4rdP15SHbb2dfY/Vw7XkiUb+yFcOcKqPlzl9ffuuY5lI9fYlrrBjxffcQOlL7o+J7GzYXXH4Bdr9r3KSoMHQ2hZjCjib2AAKbfBLc+ydY1zyWaefPvSO3fI0tlbvQnpuNIi5M/3FPLOeZc+MNCePfnxGsw1/z/5IEC9kS/nSu61hROpSiEzv0GvnV2EyZkeTl2/ncpGTfMDq/Y46/4IXn71sLGnQC4FIuighFwyX0wfGLyzzNWK+mObwhcWAav3mv/ffPv7SaBLxcOV8ChDfZv442/tX9ju3yJWoUehpXfh+1/sIPCiNqPn3ErtDbYNTLLBBSYWwa3LbdXkyf7YiX7eUh22/QbYfdr9mfi9tnNtl9Os8tiGnZTacatoIeZuetVu7bj8oCnH+vKTv3shnSoYVgmjLsYrvslHPigwy8ft73p+q/OZeaJT+1a1rFXoeV9iJyEPavA6mEd3OfQ4IQK9P7D7hSnlvOM2fDplsTfP6vu+bEphufM+XewFbr+Zkv2+K3Lum/S9MeRbYk/G1E4vBnOuw08QehYc531TTjrqsQXyO2H8263yxcbUTKi9vN5h7QHCoBlf+Fjp08mK2tfbjv1M/nTd2Dbc/b9pgb1O+0vd6z5ZGjpaWJPvLLzZz/jFruP59QynX2N3cF/vCbx+nU77fdv9WMpSZbrtfmjKMo9wD0AxcXFs3s9PP00xTZoKi0tHdDXSVkfmm4DWoZ01vK6e0893d7x9ZNdB4P3OQ3m66f62Wf6MxlkmZtR+3nxeWwXd/ee+vIlSuOEsrSUP9P/Tk4s0wCRUBFCpFVPoTIoe9T2Rcp71AohHGnwOmpTFNtJ3zF9KkKIPnFcTUUIkd0kVIQQaSWhIoRIKwkVIURaSagIIdJKQkUIkVaOG1LOyOpkIUTa9GlGraIoDUCqi39GAo39KdTnwBf5vYuuPo8/D5Mtyxqa7I4+1VQsy0p5PzxFUSq6m8b7efdFfu+iq8/jz4OiKN2u15E+FSFEWkmoCCHSaiBD5fEBfG6n+yK/d9HV5/Hnodv31KeOWiGE6I00f4QQaSWhIoRIKwkVIURaSagIIdJKQkUIkVb/C0OrRq4z+f3WAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "\n",
    "f = np.load('pca_analysis_R1.npz', allow_pickle = True)\n",
    "\n",
    "dgms = f['dgms_all'][()]\n",
    "f.close()\n",
    "\n",
    "dims = np.arange(3,41)\n",
    "lives_top3_mod1 = np.zeros((len(dims),3))\n",
    "\n",
    "lives_std_mod1 = np.zeros((len(dims),1))\n",
    "\n",
    "lives_med_mod1 = np.zeros((len(dims),1))\n",
    "\n",
    "lives_mean_mod1 = np.zeros((len(dims),1))\n",
    "\n",
    "lives_gap_mod1 = np.zeros((len(dims),1))\n",
    "\n",
    "lives_gapmax_mod1 = np.zeros((len(dims),1))\n",
    "\n",
    "lives_gap1_mod1 = np.zeros((len(dims),1))\n",
    "lives_gap2_mod1 = np.zeros((len(dims),1))\n",
    "lives_gap3_mod1 = np.zeros((len(dims),1))\n",
    "\n",
    "for it, dim in enumerate(dims):\n",
    "    diagrams = dgms[dim].copy()\n",
    "    births1 = diagrams[1][:, 0] \n",
    "    deaths1 = diagrams[1][:, 1] \n",
    "    lives1 = deaths1-births1 \n",
    "    iMax = np.argsort(lives1)\n",
    "    lives_top3_mod1[it,:] = lives1[iMax[-3:]] \n",
    "    lives_std_mod1[it,:] = np.std(lives1)\n",
    "    lives_med_mod1[it,:] = np.median(lives1)\n",
    "    lives_mean_mod1[it,:] = np.mean(lives1)\n",
    "    gaps = lives1[iMax[1:]] - lives1[iMax[:-1]]\n",
    "    lives_gap_mod1[it] = len(gaps)-np.argmax(gaps)\n",
    "    lives_gapmax_mod1[it] = np.max(gaps)\n",
    "    lives_gap1_mod1[it] = gaps[-1]\n",
    "    lives_gap2_mod1[it] = gaps[-2]\n",
    "    lives_gap3_mod1[it] = gaps[-3]\n",
    "\n",
    "    \n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(dims, np.divide(lives_top3_mod1[:,2],lives_top3_mod1[:,0]), lw = 1)\n",
    "ax.plot(dims, np.divide(lives_top3_mod1[:,1],lives_top3_mod1[:,0]), lw = 1)\n",
    "ax.scatter(dims, np.divide(lives_top3_mod1[:,2],lives_top3_mod1[:,0]), s = 10, lw = 1)\n",
    "ax.scatter(dims, np.divide(lives_top3_mod1[:,1],lives_top3_mod1[:,0]), s = 10, lw = 1)\n",
    "ax.plot([6,6], [ax.get_ylim()[0],ax.get_ylim()[1]], ls = '--',c = 'k', alpha = 0.6, lw = 2)\n",
    "ax.set_ylim([0.8, 4.2])\n",
    "ax.set_aspect(1.0/ax.get_data_ratio())\n",
    "ax.set_xticks([3,15, 50, dims[-1]])\n",
    "ax.set_xticklabels(['','','',''])\n",
    "ax.set_yticks([ 1, 2, 3,4, ])\n",
    "ax.set_yticklabels(['','','',''])\n",
    "\n",
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.plot(dims[:13], np.divide(lives_top3_mod1[:13,2],lives_top3_mod1[:13,0]), lw = 1)\n",
    "ax.plot(dims[:13], np.divide(lives_top3_mod1[:13,1],lives_top3_mod1[:13,0]), lw = 1)\n",
    "#ax.plot(dims[:13], np.divide(lives_top3_mod1[:13,0],lives_top3_mod1[:13,0]), lw = 1)\n",
    "ax.scatter(dims[:13], np.divide(lives_top3_mod1[:13,2],lives_top3_mod1[:13,0]), s = 20, lw = 1)\n",
    "ax.scatter(dims[:13], np.divide(lives_top3_mod1[:13,1],lives_top3_mod1[:13,0]), s = 20, lw = 1)\n",
    "#ax.scatter(dims[:13], np.divide(lives_top3_mod1[:13,0],lives_top3_mod1[:13,0]), s = 20, lw = 1)\n",
    "ax.plot([6,6], [ax.get_ylim()[0],ax.get_ylim()[1]], ls = '--',c = 'k', alpha = 0.6, lw = 2)\n",
    "#ax.plot([ax.get_xlim()[0],ax.get_xlim()[1]], [1,1],  ls = '--',c = 'k', alpha = 0.6, lw = 1)\n",
    "ax.set_ylim([0.8, 4.2])\n",
    "ax.set_xlim([2, 15.3])\n",
    "ax.set_aspect(1.0/ax.get_data_ratio())\n",
    "ax.set_xticks([3, 5,10,15])\n",
    "#ax.set_xticks([3, 6,11,16])\n",
    "ax.set_xticklabels(['','','',''])\n",
    "ax.set_yticks([1, 2,3, 4])\n",
    "ax.set_yticklabels(['','','',''])\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "########### Plot pca space ###################\n",
    "sig = 2.75\n",
    "ph_classes = [0,1] # Decode the ith most persistent cohomology class\n",
    "num_circ = len(ph_classes)\n",
    "dec_tresh = 0.99\n",
    "metric = 'cosine'\n",
    "maxdim = 1\n",
    "coeff = 47\n",
    "active_times = 15000\n",
    "k = 1000\n",
    "num_times = 5\n",
    "n_points = 1200\n",
    "nbs = 800\n",
    "eval_all = {}\n",
    "dev_OF = {}\n",
    "numangsint = 51\n",
    "numangsint_1 = numangsint-1\n",
    "bins = np.linspace(0,1, numangsint)\n",
    "dim = 15\n",
    "num_bins = 15\n",
    "GoGaussian = True\n",
    "nF2 = 2\n",
    "\n",
    "for rat_name, mod_name, sess_name, day_name in (('R', '1', 'OF', 'day1'),\n",
    "                                                ('R', '2', 'OF', 'day1'),\n",
    "                                                ('R', '3', 'OF', 'day1'),\n",
    "                                                ('R', '1', 'WW', 'day1'),\n",
    "                                                ('R', '2', 'WW', 'day1'),\n",
    "                                                ('R', '3', 'WW', 'day1'),\n",
    "                                                ('R', '1', 'OF', 'day2'),\n",
    "                                                ('R', '2', 'OF', 'day2'),\n",
    "                                                ('R', '3', 'OF', 'day2'),\n",
    "                                                ('R', '1', 'REM', 'day2'),\n",
    "                                                ('R', '2', 'REM', 'day2'),\n",
    "                                                ('R', '3', 'REM', 'day2'),\n",
    "                                                ('R', '1', 'SWS', 'day2'),\n",
    "                                                ('R', '2', 'SWS', 'day2'),\n",
    "                                                ('R', '3', 'SWS', 'day2'),\n",
    "                                                ('Q', '1', 'OF', ''),\n",
    "                                                ('Q', '2', 'OF', ''),\n",
    "                                                ('Q', '1', 'WW', ''),\n",
    "                                                ('Q', '2', 'WW', ''),\n",
    "                                                ('Q', '1', 'REM', ''),\n",
    "                                                ('Q', '2', 'REM', ''),\n",
    "                                                ('Q', '1', 'SWS', ''),\n",
    "                                                ('Q', '2', 'SWS', ''),\n",
    "                                                ('S', '1', 'OF', ''),\n",
    "                                                ('S', '1', 'WW', ''),\n",
    "                                                ('S', '1', 'REM', ''),\n",
    "                                                ('S', '1', 'SWS', '')\n",
    "                                                ):\n",
    "    if sess_name in ('OF','WW'):\n",
    "        sspikes,xx,yy = get_spikes(rat_name, mod_name, day_name, sess_name, bType = 'pure',\n",
    "                                         bSmooth = True, bSpeed = True)[:3]\n",
    "    else:\n",
    "        sspikes = get_spikes(rat_name, mod_name, day_name, sess_name, bType = 'pure',\n",
    "                                             bSmooth = True, bSpeed = False)\n",
    "\n",
    "\n",
    "    num_neurons = len(sspikes[0,:])            \n",
    "    times_cube = np.arange(0,len(sspikes[:,0]),num_times)\n",
    "    movetimes = np.sort(np.argsort(np.sum(sspikes[times_cube,:],1))[-active_times:])\n",
    "    movetimes = times_cube[movetimes]\n",
    "    dim_red_spikes_move_scaled, evals, __  = pca(preprocessing.scale(sspikes[movetimes,:],axis = 0), dim = dim)\n",
    "    \n",
    "    eval_all[rat_name+mod_name+sess_name+day_name] = evals\n",
    "    if sess_name in ('OF'):\n",
    "        xx = normit(xx)\n",
    "        yy = normit(yy)\n",
    "        xx = xx[movetimes]\n",
    "        yy = yy[movetimes]\n",
    "        torscores = np.zeros((dim, 4))        \n",
    "\n",
    "        for n in range(dim):\n",
    "            mtottemp, x_edge, y_edge,c2 = binned_statistic_2d(xx, yy, \n",
    "                                                  dim_red_spikes_move_scaled[:,n], statistic='mean', \n",
    "                                                  bins=bins, range=None, expand_binnumbers=True)\n",
    "            mtottemp = smooth_tuning_map(np.rot90(mtottemp), len(mtottemp)+1, sig, bClose = True)   \n",
    "            plt.figure()\n",
    "            plt.imshow(mtottemp, origin = 'lower', vmin = -2.5, vmax = 2.5)\n",
    "            for l, LAM in enumerate((np.sqrt(1),)):\n",
    "                torscores[n, l] = glm2d(np.concatenate((xx[:,np.newaxis],yy[:,np.newaxis]),1), \n",
    "                                        dim_red_spikes_move_scaled[:,n], \n",
    "                                        num_bins, True, LAM, GoGaussian, nF2)\n",
    "\n",
    "        dev_OF[rat_name+mod_name+sess_name+day_name] = torscores[:, np.argmax(np.mean(torscores,0))]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "torscores = np.zeros((dim, 4))        \n",
    "\n",
    "for n in range(dim):\n",
    "    mtottemp, x_edge, y_edge,c2 = binned_statistic_2d(xx, yy, \n",
    "                                          dim_red_spikes_move_scaled[:,n], statistic='mean', \n",
    "                                          bins=bins, range=None, expand_binnumbers=True)\n",
    "    mtottemp = smooth_tuning_map(np.rot90(mtottemp), len(mtottemp)+1, sig, bClose = True)   \n",
    "    plt.figure()\n",
    "    plt.imshow(mtottemp, origin = 'lower', vmin = -2.5, vmax = 2.5)\n",
    "    for l, LAM in enumerate((np.sqrt(1),)):\n",
    "        torscores[n, l] = glm2d(np.concatenate((xx[:,np.newaxis],yy[:,np.newaxis]),1), \n",
    "                                dim_red_spikes_move_scaled[:,n], \n",
    "                                num_bins, True, LAM, GoGaussian, nF2)\n",
    "\n",
    "dev_OF[rat_name+mod_name+sess_name+day_name] = torscores[:, np.argmax(np.mean(torscores,0))]\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for k in eval_all:                                                \n",
    "    ax.plot(np.arange(1, 16), eval_all[k], \n",
    "            lw = 1, c = colors_mods[k[:2]][0])\n",
    "    ax.scatter(np.arange(1, 16), eval_all[k], s = 20, lw = 1, \n",
    "               c = colors_envs[k])\n",
    "    ax.set_xticks([1,5,10,15])\n",
    "    ax.set_xticklabels(['','','',''])\n",
    "    ax.set_yticks([0,3,6,9])\n",
    "    ax.set_yticklabels(['','','',''])\n",
    "    ax.set_xlim([1, 15])\n",
    "    ax.set_ylim([0, 9])\n",
    "    ax.plot([6,6], [0,ax.get_ylim()[1]], ls = '--', alpha = 0.1, lw = 2, c = 'k')\n",
    "    ax.set_aspect(1.0/ax.get_data_ratio())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "\n",
    "for k in dev_OF:                                                \n",
    "    ax.plot(np.arange(1, 16), dev_OF[k], \n",
    "            lw = 1, c = colors_mods[k[:2]][0])\n",
    "    ax.scatter(np.arange(1, 16), dev_OF[k], s = 20, lw = 1, \n",
    "               c = colors_envs[k])\n",
    "    ax.set_ylim([-0.1, 0.8])\n",
    "    ax.plot([6,6], [ax.get_ylim()[0],ax.get_ylim()[1]], ls = '--', alpha = 0.1, lw = 2, c = 'k')\n",
    "    ax.set_aspect(1.0/ax.get_data_ratio())\n",
    "    ax.set_xticks([1,5,10,15])\n",
    "    ax.set_xticklabels(['','','',''])\n",
    "    ax.set_yticks([0,0.2,0.4, 0.6,0.8])\n",
    "    ax.set_yticklabels(['','','','',''])\n",
    "    ax.set_xlim([1, 15])\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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